class_docstr: This class is responsible for building components for semantic segmentation. 
code: def lr_scheduler(self) -> paddle.optimizer.lr.LRScheduler:
      lr_cfg = self.config.lr_scheduler_cfg
      assert (
          lr_cfg != {}
      ), "No lr_scheduler specified in the configuration file."
  
      use_warmup = False
      if "warmup_iters" in lr_cfg:
          use_warmup = True
          warmup_iters = lr_cfg.pop("warmup_iters")
          assert (
              "warmup_start_lr" in lr_cfg
          ), "When use warmup, please set warmup_start_lr and warmup_iters in lr_scheduler"
          warmup_start_lr = lr_cfg.pop("warmup_start_lr")
          end_lr = lr_cfg["learning_rate"]
  
      lr_type = lr_cfg.pop("type")
      if lr_type == "PolynomialDecay":
          iters = (
              self.config.iters - warmup_iters
              if use_warmup
              else self.config.iters
          )
          iters = max(iters, 1)
          lr_cfg.setdefault("decay_steps", iters)
  
      try:
          lr_sche = getattr(paddle.optimizer.lr, lr_type)(**lr_cfg)
      except Exception as e:
          raise RuntimeError(
              "Create {} has failed. Please check lr_scheduler in config. "
              "The error message: {}".format(lr_type, e)
          )
  
      if use_warmup:
          lr_sche = paddle.optimizer.lr.LinearWarmup(
              learning_rate=lr_sche,
              warmup_steps=warmup_iters,
              start_lr=warmup_start_lr,
              end_lr=end_lr,
          )
  
      return lr_sche
  
imports: import copy
  from typing import Any, Optional
  import yaml
  import paddle
  from paddleseg.cvlibs import manager, Config
  from paddleseg.utils import utils, logger
  from paddleseg.utils.utils import CachedProperty
label_desc: Use image node_id 4 for calling the SegBuilder obj's underlying member method code with example usage: obj.lr_scheduler() and returns: lr_sche